What’s a batted ball to do?

Voros McCracken made probably the single-most revolutionary (and controversial) contribution to sabermetrics when he wrote:

There is little if any difference among major-league pitchers in their ability to prevent hits on balls hit in the field of play.

A large amount of time, effort and (frankly) personal accrimony has been invested in debating this proposition. And there have been some revisions, alterations and caveats amended to the theory. But the fundamental result, which McCracken called Defense Independent Pitching Statistics, has stood up reasonably well to criticism. But there’s always room for improvement, right? Let’s go looking at some batted balls, see what we can find, shall we?

Let’s start off with ground balls, Crash Davis’ favored way of getting an out. Some pitchers simply have to be better at getting ground ball outs, don’t they, DIPS be damned?

So what affects how easy or difficult it is to field a ground ball? The most important factors are:

Where the ball is hit.

How hard the ball is hit.

What hops the ball takes.

Unfortunately, the data I have affords little opportunity to study the second and third factors. Luckily, thanks to Retrosheet we can study the first factor, and it seems to be by far the most important. Retrosheet has hit location data, although the availability varies from season to season. 1989-1999 has the most complete location data and thus formed the most fertile ground for studying the issue.

Take a look at the zone diagram, and then consider the out rate on a ground ball as it passes through those zones:

ZONE

OUTS

3

0.8697

34

0.6212

4

0.9177

4M

0.5376

5

0.7535

56

0.5894

6

0.8773

6M

0.5851

It shouldn’t come as a surprise that the easiest way to get a ground ball out is to get the batter to hit it where fielders are, rather than where they aren’t. Can a pitcher do this? First of all, we need a measure of locating the ball to test. So I came up with Good Zone Percentage, which is in essence balls in the 3, 4, 5 and 6 zones as a percentage of all ground balls. Is it possible that there’s a skill that some pitchers have that allows them to get more ground balls in the “good” zones?

It seems unlikely; looking at the year to year correlation between a pitcher’s GZP in one season to the next, I only get a figure of .07, hardly worth mentioning at all. GZP is admittedly a crude measure, and so I don’t want to entirely write off the possibility that some pitchers can control the direction of a ground ball to some extent. But if it exists, it would seem to be a very subtle effect.

And this essentially lines up with what the data about pitchers tells us. Let’s look at a different set of pitchers, from 2004-2008. (I chose this time period because my preference is to stay recent when at all possible, and 2002-2003 has spotty recording of batted ball types. Narrowing the size of the study has some other benefits as well.)

Let’s keep things simple, and thus put aside concerns (for now) of scorer bias in figuring out the difference between a popup, fly ball and line drive; let’s simply lump them all together as “air balls” for the time being. So we’ll subset batting average on balls in play into ground ball BABIP and air ball BABIP. Are either of these repeatable skills?

Essentially, what we want to measure is the persistence of a player’s apparent skill. We can do this by seeing how well a measure correlates with itself in a different sample. The most popular method of doing so is a year-to-year correlation, but we can do better than that. Instead, for all pitchers in our sample, let’s split up their BIP chances into two bins: “even” and “odd.” (In this case, the two samples were created by using a random number generator.) This gives us an average of 318 BIP per pitcher in each split half.

The first thing that we want to be careful of are the effects of a pitcher’s defense and park. If he plays in front of a particularly good (or poor) defense, or in a park that has a significant impact on BABIP rates, it could look like a persistent skill for that pitcher when it was simply an environmental factor.

So we can control for this, using the Odds Ratio method to translate a player’s performance relative to his team to his performance relative to the league. The odds ratio for a rate is:

Rate/(1-Rate)

Then, to translate the odds ratio, we use:

Pitcher_OR*League_OR/Team_OR

and then translate the ratio back into our rate. Then we run a correlation between the adjusted rates between our two split halves:

BABIP

0.096

GB

0.104

AB

0.457

So what we have here is an interesting little result: We actually do see some significant persistance in ability when it comes to air balls, at least far greater that what we see for ground balls or batted balls as a whole. In fact, the lowest correlation is for overall batted ball skill. (This can be explained rather easily and seemingly intuitively—getting outs on ground balls is negatively correlated with getting outs on fly balls, with a correlation of -.2.)

There is some sense to be had here. Think back to the list at the beginning—let’s draw up a similar list for air balls, shall we? What makes an air ball easier or harder for a fielder to catch:

Where the ball is hit.

How high the ball goes.

How fast the ball is going.

How far the ball goes.

That’s a much more robust set of variables for a pitcher to have some sort of effect on. Now, again, let’s go back to our hit location data. Unfortunately, we don’t have much to go on about how high or fast the ball went, but we do have data on how far the ball went. Dividing our Retrosheet hit location chart into seven gradients of depth (all the way from the zones marked “S” in the infield, out to the “XD” zones in the outfield) shows a variance in the rate at which air balls are converted to outs:

DEPTH

OUTS

1

0.9581

2

0.8866

3

0.5343

4

0.4467

5

0.7369

6

0.834

7

0.7925

What you want—if you’re a pitcher, at least—is either shallow air balls (typically popups on the infield) or deep air balls (so long as they stay in the park!). What you don’t want is balls the pass over the infield and land in front of the outfielders. (And as an added bonus, this is the part of the field most likely to inspire ambiguity between popups, fly balls and line drives.) So is there a persistent skill in staying out of the dead zone between the infield and the outfield?

So, similar to GZP, let’s come up with something to measure depth; let’s call it Bad Depth Percentage, and call it the number of balls in the 3-4 depth range, as a percentage of total air balls. Again looking at year-to-year correlation, we come up with .2—significant, if not hugely so. Again, BDP is a very junky sort of stat, so I’d be careful of drawing more conclusions from it than it warrants. And we are missing some important parts of the puzzle, specifically hang time. But when it comes to air balls, it seems that pitchers do have more control over where the ball is going than they do with ground balls, in ways that can (perhaps) be advantagious to themselves.

So where do we go from here? Unfortunately, right now the level of detail we have for a lot of these issues is simply “line drive, fly ball, popup,” and sometimes we’re lucky to even have that. (And sometimes, we’re not lucky at all.) This is a barrier, not only to our understanding of pitching but of fielding. Help may be on the way this season in the form of Hit f/x, the brother to MLB Advanced Media’s PITCHf/x system.

References & ResourcesThe information used here was obtained free of charge from and is copyrighted by Retrosheet. Interested parties may contact Retrosheet at “www.retrosheet.org”.

A technical note, for those interested, in the correlations presented in this article. You will often, in baseball studies, see correlations referenced along with playing-time cut-offs. Instead, all the correlations presented here are weighted correlations, using playing time (usually BIP) as the weight. (For split-half and year-to-year correlations, the smaller of the two values is used.) Using playing-time cutoffs is used to compensate for the fact that the correlations (or lack thereof) with a player who had 20 BIP are less meaningful than of a player with 300 or 1000 BIP; by correctly weighting each player by the amount of playing time, we avoid that problem altogether.